Tyndall National Institute - Conference Items

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    Development of a PPG-based hardware and software system deployable on elbow and thumb for real-time estimation of pulse transit time
    (IEEE, 2023-07-24) Valerio, Andrea; Hajzeraj, Adhurim; Talebi, Omid Varnosfaderani; Belcastro, Marco; Tedesco, Salvatore; Demarchi, Danilo; O'Flynn, Brendan; Enterprise Ireland; Science Foundation Ireland; European Regional Development Fund
    Blood pressure (BP) is a vital parameter used by clinicians to diagnose issues in the human cardiovascular system. Cuff-based BP devices are currently the standard method for on-the-spot and ambulatory BP measurements. However, cuff-based devices are not comfortable and are not suitable for long-term BP monitoring. Many studies have reported a significant correlation between pulse transit time (PTT) with blood pressure. However, this relation is impacted by many internal and external factors which might lower the accuracy of the PTT method. In this paper, we present a novel hardware system consisting of two custom photoplethysmography (PPG) sensors designed particularly for the estimation of PTT. In addition, a software interface and algorithms have been implemented to perform a real-time assessment of the PTT and other features of interest from signals gathered between the brachial artery and the thumb. A preclinical study has been conducted to validate the system. Five healthy volunteer subjects were tested and the results were then compared with those gathered using a reference device. The analysis reports a mean difference among subjects equal to -3.75±7.28 ms. Moreover, the standard deviation values obtained for each individual showed comparable results with the reference device, proving to be a valuable tool to investigate the factors impacting the BP-PTT relationship.Clinical Relevance— The proposed system proved to be a feasible solution to detect blood volume changes providing good quality signals to be used in the study of BP-PTT relationship.
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    Acoustic emissions and age-related changes of the knee
    (IEEE, 2023-07) Khokhlova, Liudmila; Dimitrios-Sokratis, Komaris; O'Flynn, Brendan; Tedesco, Salvatore; Science Foundation Ireland; European Regional Development Fund
    Acoustic emission (AE) monitoring is currently being widely investigated as a diagnostic tool in orthopedics, in particular for osteoarthritis (OA) diagnostics. Considering that age is one of the main risk factors for OA, investigating age-related changes in joint AEs might provide an additional incentive for further studies and consequent translation to clinical practice. The aim of this study is to investigate age-related changes in knee AE and determine AE hit definition modes as well as AE hit parameters that allow for improved age group differentiation. Knee AEs were recorded from 51 participants in two age groups (18-35 and 50-75 years old) whilst cycling with 30 and 60 rpm cadence. Two AE sensors with 15-40 kHz and 100-450 kHz frequency ranges were used, and three AE event detection modes investigated. Additionally, participants’ Knee Osteoarthritis Outcome Scores (KOOS) were recorded. Low frequency sensors (15-40kHz) and hit modes with shortened hit and peak definition times showed the potential to distinguish between age groups. Moreover, a weak correlation was found between only three parameters (AE event median duration, rise time, and signal strength) and age, indicating that changes in joint AE are most likely associated with pathological changes rather than physiological ageing within the healthy norm.Clinical Relevance— the use of AE monitoring was examined in the context of age-related changes in knee health. The study indicates the potential for knee AE monitoring to be used as a quantitative measure of pathological changes in the knee status.
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    Open access database of industry 4.0 tasks for the development of AI-based classifier
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-01-17) Mongelli, Francesca; Menolotto, Matteo; O'Flynn, Brendan; Demarchi, Danilo; Larcher, L.; Science Foundation Ireland; European Regional Development Fund
    Robots and humans coworkers are sharing more and more portions of the smart manufacturing globally, meeting the need for high flexibility and rapid changes in the production layout. To be fully effective, however, such transition from classic robotics to the so-called collaborative robotics has to address several open problems, mostly related with safety and task optimization. Promising answers are coming from the motion capture technology, where wearable and optoelectronic sensing devices are deployed to gather human centric data to provide the robots with some form of awareness respect with the human activity and position. Tracking the hand of the operator, in particular, offers many advantages as we use our hands to explore and interact with the surroundings and to communicate. This has been highlighted by the several works focusing on gesture hand configuration recognition. This work present HANDMI4, a new open access database of hand motion tracking data, which includes a wide range of static hand grasp configurations and some classic dynamic industry tasks. Such database was generated using two of the most mature technologies for motion capture: IMU-based data glove and camera-based triangulation. To test the capability of such dataset to foster AI-based task classifier, a set of machine learning techniques were implemented and tested. In particular, KNN weighted reached 94,4% and 100% of task classification accuracy for the data glove and the camera system, respectively. With this open access database we aim to boost the research around task classification through motion capture technology to enable the next revolution in smart manufacturing.
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    Validation of endurance model for manual tasks*
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-12-11) O’Sullivan, Patricia; Menolotto, Matteo; O’Flynn, Brendan; Komaris, Dimitrios-Sokratis; Science Foundation Ireland
    Physical fatigue in the workplace can lead to work-related musculoskeletal disorders (WMSDs), especially in occupations that require repetitive, mid-air movements, such as manufacturing and assembly tasks in industry settings. The current paper endeavors to validate an existing torque-based fatigue prediction model for lifting tasks. The model uses anthropometrics and the maximum torque of the individual to predict the time to fatigue. Twelve participants took part in the study which measured body composition parameters and the maximum force produced by the shoulder joint in flexion, followed by three lifting tasks for the shoulder in flexion, including isometric and dynamic tasks with one and two hands. Inertial measurements units (IMUs) were worn by participants to determine the torque at each instant to calculate the endurance time and CE, while a self-subjective questionnaire was utilized to assess physical exertion, the Borg Rate of Perceived Exertion (RPE) scale. The model was effective for static and two-handed tasks and produced errors in the range of [28.62 49.21] for the last task completed, indicating the previous workloads affect the endurance time, even though the individual perceives they are fully rested. The model was not effective for the one-handed dynamic task and differences were observed between males and females, which will be the focus of future work.An individualized, torque-based fatigue prediction model, such as the model presented, can be used to design worker-specific target levels and workloads, take inter and intra individual differences into account, and put fatigue mitigating interventions into place before fatigue occurs; resulting in potentially preventing WMSDs, aiding in worker wellbeing and benefitting the quality and efficiency of the work output.Clinical Relevance— This research provides the basis for an individualized, torque-based approach to the prediction of fatigue at the shoulder joint which can be used to assign worker tasks and rest breaks, design worker specific targets and reduce the prevalence of work-related musculoskeletal disorders in occupational settings.
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    Patterned InterVia for heterogeneous integration of III-V devices onto silicon photonics by using micro-transfer printing
    (2023) Uzun, Ali; Rimböck, Johanna; Gasiorowski, Jacek; Farrell, Alex; Fecioru, Alin; Loi, Ruggero; Corbett, Brian; Horizon 2020; Science Foundation Ireland